Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Sparse Spiking Gradient Descent
Authors: Nicolas Perez-Nieves, Dan Goodman
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our method on real datasets of varying complexity (Fashion-MNIST, Neuromophic MNIST and Spiking Heidelberg Digits) achieving a speedup in the backward pass of up to 150x, and 85% more memory efficient, without losing accuracy. |
| Researcher Affiliation | Academia | Nicolas Perez-Nieves Electrical and Electronic Engineering Imperial College London London, United Kingdom EMAIL Dan F.M. Goodman Electrical and Electronic Engineering Imperial College London London, United Kingdom EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks found. |
| Open Source Code | No | No explicit statement or link providing open-source code for the methodology described in this paper. |
| Open Datasets | Yes | Fashion-MNIST dataset (F-MNIST) [44], Neuromorphic MNIST (N-MNIST) [45] dataset... Spiking Heidelberg Dataset (SHD) [46]) |
| Dataset Splits | No | No explicit details on train/validation/test splits are provided in the main text. It refers to Appendix E for training details, which is not available. |
| Hardware Specification | Yes | Figure 4 was obtained from running on an RTX6000 GPU. We also run this on smaller GPUs (GTX1060 and GTX1080Ti) |
| Software Dependencies | No | The paper mentions 'Pytorch CUDA extension' but does not specify version numbers for PyTorch or CUDA, nor any other software dependencies with versions. |
| Experiment Setup | No | The paper mentions a 'three-layer fully connected network' and the surrogate gradient function 'g(V ) := 1/(β|V Vth| + 1)2'. However, it states 'See Appendix E for all training details,' and Appendix E is not provided in the paper, hence complete experimental setup details like specific hyperparameters are missing from the main text. |